This disclosure relates generally to a computer-based, real-time system for event probability prediction that implements an efficient profiling technology which minimizes required computer resources and allows for the identification of entities exhibiting anomalous behavior.
Computer-based event probability prediction systems traditionally use some amount of historical information, a profile, about individual objects in order to compare present behavior with past behavior. Each of these objects is defined to be an entity, while a set of similar objects is defined to be an entity class. Examples of events to predict include whether or not a loan applicant will default on a loan and whether or not a credit card transaction is fraudulent. Examples of entities include a particular customer account at a bank and a particular Automatic Teller Machine (ATM).
To achieve high performance, an event probability prediction system often includes a mathematical model or combination of models which extracts patterns from historical data and uses the patterns on the present transaction data to calculate a score, a number that represents the likelihood that a particular event will occur. The model or models in the system traditionally need to store and access the profile for every existing entity in the entity class (e.g. every ATM being considered in the problem). Limitations of computer resources require that such a large amount of information is maintained in a disk-resident profile database, external to the computer program forming the core of the event probability prediction system. This leads to several issues in the development and running of the event probability prediction system:
1) It is necessary to create an interface between the mathematical model and the external database containing the profiles during development of the event probability prediction system.
2) It is necessary to create an interface between the mathematical model and the external database containing the profiles in the production environment in which the system will ultimately be used.
3) The system's capacity to process transactions may be severely limited due to the required interface with an external database.
Each of these issues could be a potential problem making the development and/or installation of the event probability prediction system infeasible.
Furthermore, in addition to the strain a traditional system places on the computer resources available, such a system may not allow the user to easily identify those entities which display a behavior of interest, particularly when multiple entity classes are being profiled to provide a multi-dimensional view of the data. Effective event probability prediction requires that only the minimum set of entities, a set whose membership varies over time, be profiled and maintained in a data store. It would be advantageous to provide a system and method that solves any of or any combination of the problems disclosed hereinabove.